As an emerging paradigm that distributes cloud computing capability to the network edge, mobile edge computing (MEC) supports computation-hungry and latency-sensitive mobile applications and is a key component in 5G mobile networks. In this thesis, we consider MEC-enabled multi-user systems where each base station is equipped with an MEC server that can provide computational services to mobile users via task offloading. Due to the limited resources in MEC systems, it is critical to develop effective joint radio and computational resource management policies. However, most existing works focus on single-objective resource allocation for MEC systems, which aims either to minimize task latency with energy budgets or to minimize device energy consumption with latency constraints, bu...[ Read more ]

As an emerging paradigm that distributes cloud computing capability to the network edge, mobile edge computing (MEC) supports computation-hungry and latency-sensitive mobile applications and is a key component in 5G mobile networks. In this thesis, we consider MEC-enabled multi-user systems where each base station is equipped with an MEC server that can provide computational services to mobile users via task offloading. Due to the limited resources in MEC systems, it is critical to develop effective joint radio and computational resource management policies. However, most existing works focus on single-objective resource allocation for MEC systems, which aims either to minimize task latency with energy budgets or to minimize device energy consumption with latency constraints, but not both. However, task latency and mobile energy consumption are both critical aspects of users’ experience. Therefore, we investigate multi-objective resource allocation in this thesis.

We first address the multi-objective resource allocation problem for multi-user single-server MEC systems by adopting the system utility, which is a normalized weighted combination of the time and energy saving achieved by computation offloading, as the performance metric. To provide an efficient solution, a low-complexity ranking-based algorithm is proposed based on the modified Newton method and the concept of computation offloading priority. Simulation results show that our proposed algorithm achieves a near-optimal performance and greatly outperforms a baseline algorithm with random spectrum allocation. In addition, it is demonstrated that jointly optimizing the spectrum and computational resource management is more critical in a large-scale network.

We then consider more general multi-user multi-server MEC systems, where inter-cell interference cannot be neglected and server selection brings a new challenge. The resource management problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem effectively, we propose two algorithms, named the Heu-Con and the Heu-Appro algorithm, respectively, both based on heuristic task offloading algorithms. To solve the resource allocation problem with a given offloading policy, the Heu-Con algorithm uses a conventional method with the help of a convex concave procedure (CCCP), while the Heu-Appro algorithm considers an approximate problem. It is shown that these two proposed algorithms achieve a performance gain compared with a baseline algorithm that ignores inter-cell interference in its design. Moreover, the Heu-Appro algorithm performs closely to the optimal solution and significantly decreases the average time and energy consumption.